TY - JOUR
T1 - Using diffusion anisotropy to characterize neuronal morphology in gray matter
T2 - The orientation distribution of axons and dendrites in the NeuroMorpho.org database
AU - Hansen, Mikkel B.
AU - Jespersen, Sune N.
AU - Leigland, Lindsey A.
AU - Kroenke, Christopher D.
PY - 2013
Y1 - 2013
N2 - Accurate mathematical modeling is integral to the ability to interpret diffusion magnetic resonance (MR) imaging data in terms of cellular structure in brain gray matter (GM). In previous work, we derived expressions to facilitate the determination of the orientation distribution of axonal and dendritic processes from diffusion MR data. Here we utilize neuron reconstructions available in the NeuroMorpho database (www.neuromorpho.org) to assess the validity of the model we proposed by comparing morphological properties of the neurons to predictions based on diffusion MR simulations using the reconstructed neuron models. Initially, the method for directly determining neurite orientation distributions is shown to not depend on the line length used to quantify cylindrical elements. Further variability in neuron morphology is characterized relative to neuron type, species, and laboratory of origin. Subsequently, diffusion MR signals are simulated based on human neocortical neuron reconstructions. This reveals a bias in which diffusion MR data predict neuron orientation distributions to have artificially low anisotropy. This bias is shown to arise from shortcomings (already at relatively low diffusion weighting) in the Gaussian approximation of diffusion, in the presence of restrictive barriers, and data analysis methods involving higher moments of the cumulant expansion are shown to be capable of reducing the magnitude of the observed bias.
AB - Accurate mathematical modeling is integral to the ability to interpret diffusion magnetic resonance (MR) imaging data in terms of cellular structure in brain gray matter (GM). In previous work, we derived expressions to facilitate the determination of the orientation distribution of axonal and dendritic processes from diffusion MR data. Here we utilize neuron reconstructions available in the NeuroMorpho database (www.neuromorpho.org) to assess the validity of the model we proposed by comparing morphological properties of the neurons to predictions based on diffusion MR simulations using the reconstructed neuron models. Initially, the method for directly determining neurite orientation distributions is shown to not depend on the line length used to quantify cylindrical elements. Further variability in neuron morphology is characterized relative to neuron type, species, and laboratory of origin. Subsequently, diffusion MR signals are simulated based on human neocortical neuron reconstructions. This reveals a bias in which diffusion MR data predict neuron orientation distributions to have artificially low anisotropy. This bias is shown to arise from shortcomings (already at relatively low diffusion weighting) in the Gaussian approximation of diffusion, in the presence of restrictive barriers, and data analysis methods involving higher moments of the cumulant expansion are shown to be capable of reducing the magnitude of the observed bias.
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U2 - 10.3389/fnint.2013.00031
DO - 10.3389/fnint.2013.00031
M3 - Article
C2 - 23675327
AN - SCOPUS:84876357467
SN - 1662-5145
SP - 1
EP - 31
JO - Frontiers in Integrative Neuroscience
JF - Frontiers in Integrative Neuroscience
IS - APR 2013
ER -